{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7_gtFoV8BuRx"
      },
      "source": [
        "测试视频效果的脚本\n",
        "This is a simple example of SimSwap on processing video with multiple faces. You can change the codes for inference based on our other scripts for image or single face swapping.\n",
        "\n",
        "Code path: https://github.com/neuralchen/SimSwap\n",
        "\n",
        "Paper path: https://arxiv.org/pdf/2106.06340v1.pdf or https://dl.acm.org/doi/10.1145/3394171.3413630"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Y1RfpzsCAl9",
        "outputId": "a39470a0-9689-409d-a0a4-e2afd5d3b5dd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mon Jun 21 02:13:20 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 465.27       Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   45C    P8    10W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "## make sure you are using a runtime with GPU\n",
        "## you can check at Runtime/Change runtime type in the top bar.\n",
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Qzzx2UpDkqw"
      },
      "source": [
        "## Installation\n",
        "\n",
        "All file changes made by this notebook are temporary. \n",
        "You can try to mount your own google drive to store files if you want.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VA_4CeWZCHLP",
        "outputId": "4b0f176f-87e7-4772-8b47-c2098d8f3bf6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'SimSwap'...\n",
            "remote: Enumerating objects: 362, done.\u001b[K\n",
            "remote: Counting objects: 100% (362/362), done.\u001b[K\n",
            "remote: Compressing objects: 100% (281/281), done.\u001b[K\n",
            "remote: Total 362 (delta 149), reused 272 (delta 67), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (362/362), 101.31 MiB | 32.47 MiB/s, done.\n",
            "Resolving deltas: 100% (149/149), done.\n",
            "Already up to date.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/neuralchen/SimSwap\n",
        "!cd SimSwap && git pull"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y5K4au_UCkKn",
        "outputId": "9691a7a4-192e-4ec2-c3c1-1f2c933d7b6a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting insightface==0.2.1\n",
            "  Downloading https://files.pythonhosted.org/packages/ee/1e/6395bbe0db665f187c8e49266cda54fcf661f182192370d409423e4943e4/insightface-0.2.1-py2.py3-none-any.whl\n",
            "Collecting onnxruntime\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f9/76/3d0f8bb2776961c7335693df06eccf8d099e48fa6fb552c7546867192603/onnxruntime-1.8.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.5MB)\n",
            "\u001b[K     |████████████████████████████████| 4.5MB 10.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: moviepy in /usr/local/lib/python3.7/dist-packages (0.2.3.5)\n",
            "Collecting onnx\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/3f/9b/54c950d3256e27f970a83cd0504efb183a24312702deed0179453316dbd0/onnx-1.9.0-cp37-cp37m-manylinux2010_x86_64.whl (12.2MB)\n",
            "\u001b[K     |████████████████████████████████| 12.2MB 51.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (3.2.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (4.41.1)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (7.1.2)\n",
            "Requirement already satisfied: scikit-image in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (0.16.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (2.23.0)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (0.22.2.post1)\n",
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (4.1.2.30)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (1.19.5)\n",
            "Requirement already satisfied: easydict in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (1.9)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from insightface==0.2.1) (1.4.1)\n",
            "Requirement already satisfied: flatbuffers in /usr/local/lib/python3.7/dist-packages (from onnxruntime) (1.12)\n",
            "Requirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (from onnxruntime) (3.12.4)\n",
            "Requirement already satisfied: decorator<5.0,>=4.0.2 in /usr/local/lib/python3.7/dist-packages (from moviepy) (4.4.2)\n",
            "Requirement already satisfied: imageio<3.0,>=2.1.2 in /usr/local/lib/python3.7/dist-packages (from moviepy) (2.4.1)\n",
            "Requirement already satisfied: typing-extensions>=3.6.2.1 in /usr/local/lib/python3.7/dist-packages (from onnx->insightface==0.2.1) (3.7.4.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from onnx->insightface==0.2.1) (1.15.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->insightface==0.2.1) (1.3.1)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->insightface==0.2.1) (2.8.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->insightface==0.2.1) (0.10.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->insightface==0.2.1) (2.4.7)\n",
            "Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->insightface==0.2.1) (2.5.1)\n",
            "Requirement already satisfied: PyWavelets>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->insightface==0.2.1) (1.1.1)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->insightface==0.2.1) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->insightface==0.2.1) (2021.5.30)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->insightface==0.2.1) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->insightface==0.2.1) (1.24.3)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->insightface==0.2.1) (1.0.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from protobuf->onnxruntime) (57.0.0)\n",
            "Installing collected packages: onnx, insightface, onnxruntime\n",
            "Successfully installed insightface-0.2.1 onnx-1.9.0 onnxruntime-1.8.0\n",
            "Requirement already satisfied: googledrivedownloader in /usr/local/lib/python3.7/dist-packages (0.4)\n",
            "Requirement already satisfied: imageio==2.4.1 in /usr/local/lib/python3.7/dist-packages (2.4.1)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from imageio==2.4.1) (7.1.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from imageio==2.4.1) (1.19.5)\n"
          ]
        }
      ],
      "source": [
        "!pip install insightface==0.2.1 onnxruntime moviepy\n",
        "!pip install googledrivedownloader\n",
        "!pip install imageio==2.4.1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gQ7ZoIbLFCye",
        "outputId": "bb35e7e2-14b7-4f36-d62a-499ba041cf64"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " crop_224\t    models\t\t   test_one_image.py\n",
            " data\t\t    options\t\t   test_video_swapmulti.py\n",
            " demo_file\t    output\t\t   test_video_swapsingle.py\n",
            " doc\t\t    README.md\t\t   test_wholeimage_swapmulti.py\n",
            " insightface_func  'SimSwap colab.ipynb'   test_wholeimage_swapsingle.py\n",
            " LICENSE\t    simswaplogo\t\t   util\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "os.chdir(\"SimSwap\")\n",
        "!ls"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gLti1J0pEFjJ",
        "outputId": "e93c3f98-01df-458e-b791-c32f7343e705"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading 1TLNdIufzwesDbyr_nVTR7Zrx9oRHLM_N into ./arcface_model/arcface_checkpoint.tar... Done.\n",
            "Downloading 1PXkRiBUYbu1xWpQyDEJvGKeqqUFthJcI into ./checkpoints.zip... Done.\n",
            "Archive:  ./checkpoints.zip\n",
            "   creating: ./checkpoints/people/\n",
            "  inflating: ./checkpoints/people/iter.txt  \n",
            "  inflating: ./checkpoints/people/latest_net_D1.pth  \n",
            "  inflating: ./checkpoints/people/latest_net_D2.pth  \n",
            "  inflating: ./checkpoints/people/latest_net_G.pth  \n",
            "  inflating: ./checkpoints/people/loss_log.txt  \n",
            "  inflating: ./checkpoints/people/opt.txt  \n",
            "   creating: ./checkpoints/people/web/\n",
            "   creating: ./checkpoints/people/web/images/\n"
          ]
        }
      ],
      "source": [
        "from google_drive_downloader import GoogleDriveDownloader\n",
        "\n",
        "### it seems that google drive link may not be permenant, you can find this ID from our open url.\n",
        "# GoogleDriveDownloader.download_file_from_google_drive(file_id='1TLNdIufzwesDbyr_nVTR7Zrx9oRHLM_N',\n",
        "#                                     dest_path='./arcface_model/arcface_checkpoint.tar')\n",
        "# GoogleDriveDownloader.download_file_from_google_drive(file_id='1PXkRiBUYbu1xWpQyDEJvGKeqqUFthJcI',\n",
        "#                                     dest_path='./checkpoints.zip')\n",
        "\n",
        "!wget -P ./arcface_model https://github.com/neuralchen/SimSwap/releases/download/1.0/arcface_checkpoint.tar\n",
        "!wget https://github.com/neuralchen/SimSwap/releases/download/1.0/checkpoints.zip\n",
        "!unzip ./checkpoints.zip  -d ./checkpoints\n",
        "!wget -P ./parsing_model/checkpoint https://github.com/neuralchen/SimSwap/releases/download/1.0/79999_iter.pth"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aSRnK5V4HI-k",
        "outputId": "e688746c-c33a-485c-808c-54a7370f0c53"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2021-06-21 02:14:17--  https://sh23tw.dm.files.1drv.com/y4mmGiIkNVigkSwOKDcV3nwMJulRGhbtHdkheehR5TArc52UjudUYNXAEvKCii2O5LAmzGCGK6IfleocxuDeoKxDZkNzDRSt4ZUlEt8GlSOpCXAFEkBwaZimtWGDRbpIGpb_pz9Nq5jATBQpezBS6G_UtspWTkgrXHHxhviV2nWy8APPx134zOZrUIbkSF6xnsqzs3uZ_SEX_m9Rey0ykpx9w\n",
            "Resolving sh23tw.dm.files.1drv.com (sh23tw.dm.files.1drv.com)... 13.107.42.12\n",
            "Connecting to sh23tw.dm.files.1drv.com (sh23tw.dm.files.1drv.com)|13.107.42.12|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 248024513 (237M) [application/zip]\n",
            "Saving to: ‘antelope.zip’\n",
            "\n",
            "antelope.zip        100%[===================>] 236.53M  6.16MB/s    in 31s     \n",
            "\n",
            "2021-06-21 02:14:48 (7.66 MB/s) - ‘antelope.zip’ saved [248024513/248024513]\n",
            "\n",
            "Archive:  ./antelope.zip\n",
            "   creating: ./insightface_func/models/antelope/\n",
            "  inflating: ./insightface_func/models/antelope/glintr100.onnx  \n",
            "  inflating: ./insightface_func/models/antelope/scrfd_10g_bnkps.onnx  \n"
          ]
        }
      ],
      "source": [
        "## You can upload filed manually\n",
        "# from google.colab import drive\n",
        "# drive.mount('/content/gdrive')\n",
        "\n",
        "### Now onedrive file can be downloaded in Colab directly!\n",
        "### If the link blow is not permanent, you can just download it from the \n",
        "### open url(can be found at [our repo]/doc/guidance/preparation.md) and copy the assigned download link here.\n",
        "### many thanks to woctezuma for this very useful help\n",
        "!wget --no-check-certificate \"https://sh23tw.dm.files.1drv.com/y4mmGiIkNVigkSwOKDcV3nwMJulRGhbtHdkheehR5TArc52UjudUYNXAEvKCii2O5LAmzGCGK6IfleocxuDeoKxDZkNzDRSt4ZUlEt8GlSOpCXAFEkBwaZimtWGDRbpIGpb_pz9Nq5jATBQpezBS6G_UtspWTkgrXHHxhviV2nWy8APPx134zOZrUIbkSF6xnsqzs3uZ_SEX_m9Rey0ykpx9w\" -O antelope.zip\n",
        "!unzip ./antelope.zip -d ./insightface_func/models/\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BsGmIMxLVxyO"
      },
      "source": [
        "## Inference"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PfSsND36EMvn",
        "outputId": "f28c98fd-4c6d-40fa-e3c7-99b606c7492a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Imageio: 'ffmpeg-linux64-v3.3.1' was not found on your computer; downloading it now.\n",
            "Try 1. Download from https://github.com/imageio/imageio-binaries/raw/master/ffmpeg/ffmpeg-linux64-v3.3.1 (43.8 MB)\n",
            "Downloading: 8192/45929032 bytes (0.0%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b1286144/45929032 bytes (2.8%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b3653632/45929032 bytes (8.0%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b7479296/45929032 bytes (16.3%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b11526144/45929032 bytes (25.1%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b15171584/45929032 bytes (33.0%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b18997248/45929032 bytes (41.4%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b22724608/45929032 bytes (49.5%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b26673152/45929032 bytes (58.1%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b30728192/45929032 bytes (66.9%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b34725888/45929032 bytes (75.6%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b38879232/45929032 bytes (84.7%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b42680320/45929032 bytes (92.9%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b45929032/45929032 bytes (100.0%)\n",
            "  Done\n",
            "File saved as /root/.imageio/ffmpeg/ffmpeg-linux64-v3.3.1.\n"
          ]
        }
      ],
      "source": [
        "import cv2\n",
        "import torch\n",
        "import fractions\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "import torch.nn.functional as F\n",
        "from torchvision import transforms\n",
        "from models.models import create_model\n",
        "from options.test_options import TestOptions\n",
        "from insightface_func.face_detect_crop_multi import Face_detect_crop\n",
        "from util.videoswap import video_swap\n",
        "from util.add_watermark import watermark_image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "rxSbZ2EDNDlf"
      },
      "outputs": [],
      "source": [
        "transformer = transforms.Compose([\n",
        "        transforms.ToTensor(),\n",
        "        #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "    ])\n",
        "\n",
        "transformer_Arcface = transforms.Compose([\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "    ])\n",
        "\n",
        "detransformer = transforms.Compose([\n",
        "        transforms.Normalize([0, 0, 0], [1/0.229, 1/0.224, 1/0.225]),\n",
        "        transforms.Normalize([-0.485, -0.456, -0.406], [1, 1, 1])\n",
        "    ])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wwJOwR9LNKRz",
        "outputId": "bdc82f7b-21c4-403f-94d1-b92911698b4a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "------------ Options -------------\n",
            "Arc_path: models/BEST_checkpoint.tar\n",
            "aspect_ratio: 1.0\n",
            "batchSize: 8\n",
            "checkpoints_dir: ./checkpoints\n",
            "cluster_path: features_clustered_010.npy\n",
            "data_type: 32\n",
            "dataroot: ./datasets/cityscapes/\n",
            "display_winsize: 512\n",
            "engine: None\n",
            "export_onnx: None\n",
            "f: /root/.local/share/jupyter/runtime/kernel-6d955151-4911-464a-824d-f0806d8071f6.json\n",
            "feat_num: 3\n",
            "fineSize: 512\n",
            "fp16: False\n",
            "gpu_ids: [0]\n",
            "how_many: 50\n",
            "image_size: 224\n",
            "input_nc: 3\n",
            "instance_feat: False\n",
            "isTrain: False\n",
            "label_feat: False\n",
            "label_nc: 0\n",
            "latent_size: 512\n",
            "loadSize: 1024\n",
            "load_features: False\n",
            "local_rank: 0\n",
            "max_dataset_size: inf\n",
            "model: pix2pixHD\n",
            "nThreads: 2\n",
            "n_blocks_global: 6\n",
            "n_blocks_local: 3\n",
            "n_clusters: 10\n",
            "n_downsample_E: 4\n",
            "n_downsample_global: 3\n",
            "n_local_enhancers: 1\n",
            "name: people\n",
            "nef: 16\n",
            "netG: global\n",
            "ngf: 64\n",
            "niter_fix_global: 0\n",
            "no_flip: False\n",
            "no_instance: False\n",
            "norm: batch\n",
            "norm_G: spectralspadesyncbatch3x3\n",
            "ntest: inf\n",
            "onnx: None\n",
            "output_nc: 3\n",
            "output_path: ./output/\n",
            "phase: test\n",
            "pic_a_path: ./crop_224/gdg.jpg\n",
            "pic_b_path: ./crop_224/zrf.jpg\n",
            "resize_or_crop: scale_width\n",
            "results_dir: ./results/\n",
            "semantic_nc: 3\n",
            "serial_batches: False\n",
            "temp_path: ./temp_results\n",
            "tf_log: False\n",
            "use_dropout: False\n",
            "use_encoded_image: False\n",
            "verbose: False\n",
            "video_path: ./demo_file/multi_people_1080p.mp4\n",
            "which_epoch: latest\n",
            "-------------- End ----------------\n",
            "input mean and std: 127.5 127.5\n",
            "find model: ./insightface_func/models/antelope/glintr100.onnx recognition\n",
            "find model: ./insightface_func/models/antelope/scrfd_10g_bnkps.onnx detection\n",
            "set det-size: (640, 640)\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r  0%|          | 0/594 [00:00<?, ?it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(142, 366, 4)\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 594/594 [08:45<00:00,  1.13it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[MoviePy] >>>> Building video ./output/demo.mp4\n",
            "[MoviePy] Writing audio in demoTEMP_MPY_wvf_snd.mp3\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 438/438 [00:00<00:00, 877.18it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[MoviePy] Done.\n",
            "[MoviePy] Writing video ./output/demo.mp4\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 595/595 [00:53<00:00, 11.15it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[MoviePy] Done.\n",
            "[MoviePy] >>>> Video ready: ./output/demo.mp4 \n",
            "\n"
          ]
        }
      ],
      "source": [
        "opt = TestOptions()\n",
        "opt.initialize()\n",
        "opt.parser.add_argument('-f') ## dummy arg to avoid bug\n",
        "opt = opt.parse()\n",
        "opt.pic_a_path = './demo_file/Iron_man.jpg' ## or replace it with image from your own google drive\n",
        "opt.video_path = './demo_file/multi_people_1080p.mp4' ## or replace it with video from your own google drive\n",
        "opt.output_path = './output/demo.mp4'\n",
        "opt.temp_path = './tmp'\n",
        "opt.Arc_path = './arcface_model/arcface_checkpoint.tar'\n",
        "opt.isTrain = False\n",
        "opt.use_mask = True  ## new feature up-to-date\n",
        "\n",
        "crop_size = opt.crop_size\n",
        "\n",
        "torch.nn.Module.dump_patches = True\n",
        "model = create_model(opt)\n",
        "model.eval()\n",
        "\n",
        "app = Face_detect_crop(name='antelope', root='./insightface_func/models')\n",
        "app.prepare(ctx_id= 0, det_thresh=0.6, det_size=(640,640))\n",
        "\n",
        "with torch.no_grad():\n",
        "    pic_a = opt.pic_a_path\n",
        "    # img_a = Image.open(pic_a).convert('RGB')\n",
        "    img_a_whole = cv2.imread(pic_a)\n",
        "    img_a_align_crop, _ = app.get(img_a_whole,crop_size)\n",
        "    img_a_align_crop_pil = Image.fromarray(cv2.cvtColor(img_a_align_crop[0],cv2.COLOR_BGR2RGB)) \n",
        "    img_a = transformer_Arcface(img_a_align_crop_pil)\n",
        "    img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])\n",
        "\n",
        "    # convert numpy to tensor\n",
        "    img_id = img_id.cuda()\n",
        "\n",
        "    #create latent id\n",
        "    img_id_downsample = F.interpolate(img_id, size=(112,112))\n",
        "    latend_id = model.netArc(img_id_downsample)\n",
        "    latend_id = latend_id.detach().to('cpu')\n",
        "    latend_id = latend_id/np.linalg.norm(latend_id,axis=1,keepdims=True)\n",
        "    latend_id = latend_id.to('cuda')\n",
        "\n",
        "    video_swap(opt.video_path, latend_id, model, app, opt.output_path, temp_results_dir=opt.temp_path, use_mask=opt.use_mask)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "SimSwap colab.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
